Predictive biomarkers for ovarian cancer

ABSTRACT

Methods are provided for predicting the presence, subtype and stage of ovarian cancer, as well as for assessing the therapeutic efficacy of a cancer treatment and determining whether a subject potentially is developing cancer. Associated test kits, computer and analytical systems as well as software and diagnostic models are also provided.

STATEMENT OF PRIORITY

This application claims priority under 35 USC Section 119 to ProvisionalPatent Application Ser. No. 60/947,253 filed Jun. 29, 2007 and61/037,946 filed Mar. 19, 2008, the disclosures of which are herebyincorporated by reference in their entireties.

FIELD OF THE INVENTION

This invention provides methods for predicting and diagnosing ovariancancer, particularly epithelial ovarian cancer, and it further providesassociated analytical reagents, diagnostic models, test kits andclinical reports.

BACKGROUND

The American Cancer Society estimates that ovarian cancer will strike22,430 women and take the lives of 15,280 women in 2007 in the UnitedStates. Ovarian cancer is not a single disease, however, and there areactually more than 30 types and subtypes of ovarian malignancies, eachwith its own pathology and clinical behavior. Most experts thereforegroup ovarian cancers within three major categories, according to thekind of cells from which they were formed: epithelial tumors arise fromcells that line or cover the ovaries; germ cell tumors originate fromcells that are destined to form eggs within the ovaries; and sexcord-stromal cell tumors begin in the connective cells that hold theovaries together and produce female hormones.

Common epithelial tumors begin in the surface epithelium of the ovariesand account for about 90 percent of all ovarian cancers in the U.S. (andthe following percentages reflect U.S. prevalence of these cancers).They are further divided into a number of subtypes—including serous,endometrioid, mucinous, and clear cell tumors—that can be furthersubclassified as benign or malignant tumors. Serous tumors are the mostwidespread forms of ovarian cancer. They account for 40 percent ofcommon epithelial tumors. About 50 percent of these serous tumors aremalignant, 33 percent are benign, and 17 percent are of borderlinemalignancy. Serous tumors occur most often in women who are between 40and 60 years of age.

Endometrioid tumors represent approximately 20 percent of commonepithelial tumors. In about 20 percent of individuals, these cancers areassociated with endometrial carcinoma (cancer of the womb lining). In 5percent of cases, they also are linked with endometriosis, an abnormaloccurrence of endometrium (womb lining tissue) within the pelvic cavity.The majority (about 80 percent) of these tumors are malignant, and theremainder (roughly 20 percent) usually is borderline malignancies.Endometrioid tumors occur primarily in women who are between 50 and 70years of age.

Clear cell tumors account for about 6 percent of common epithelialtumors. Nearly all of these tumors are malignant. Approximately one-halfof all clear cell tumors are associated with endometriosis. Mostpatients with clear cell tumors are between 40 and 80 years of age.

Mucinous tumors make up about 1 percent of all common epithelial tumors.Most (approximately 80 percent) of these tumors are benign, 15 percentare of borderline malignancy, and only 5 percent are malignant. Mucinoustumors appear most often in women between 30 to 50 years of age.

Ovarian cancer is by far the most deadly of gynecologic cancers,accounting for more than 55 percent of all gynecologic cancer deaths.But ovarian cancer is also among the most treatable—if it is caughtearly. When ovarian cancer is caught early and appropriately treated,the 5-year survival rate is 93 percent. See, for example, Luce et al,“Early Diagnosis Key to Epithelial Ovarian Cancer Detection,” The NursePractitioner, December 2003 at p. 41. Extensive background informationabout ovarian cancer is readily available on the internet, for example,from the “Overview: Ovarian Cancer” of the Cancer Reference Informationprovided by the American Cancer Society (www.cancer.org) and the NCCNClinical Practice Guidelines in Oncology™ Ovarian Cancer V.1.2007(www.nccn.org).

The current reality for the diagnosis of ovarian cancer is that mostcases—81 percent of all cases of ovarian cancer—are not caught inearliest stage. This is because early stage ovarian cancer is verydifficult to diagnose. Its symptoms may not appear or be noticed at thispoint. Or, symptoms—such as bloating, indigestion, diarrhea,constipation and others—may be vague and associated with many common andless serious conditions. Most importantly, there has been no effectivetest for early detection. An effective tool for early and accuratedetection of ovarian cancer is a critical unmet medical need.

Biomarkers for Ovarian Cancer

A variety of biomarkers to diagnose ovarian cancer have been proposed,and elucidated through a variety of technology platforms and dataanalysis tools. An interesting compilation of 1,261 potential proteinbiomarkers for various pathologies was presented by N. Leigh Anderson etal., “A Target List of Candidate Biomarkers for Targeted Proteomics,”Biomarker Insights 2:1-48 (2006). A spreadsheet listing the markersdiscussed in this paper can be found at the website of the PlasmaProteome Institute (http://www.plasmaproteome.org). Several publishedstudies are described immediately below and a number of other studiesare listed as references at the end of this specification. All of thesestudies, all other documents cited in this specification, and relatedprovisional patent application Ser. No. 60/947,253 filed Jun. 29, 2007and 61/037,946 filed Mar. 19, 2008, are hereby incorporated by referencein their entireties.

For example, Cole, “Methods for detecting the onset, progression andregression of gynecologic cancers,” U.S. Pat. No. 5,356,817 (Oct. 18,1994) described a method for detecting the presence of a gynecologiccancer in a female, said cancer selected from the group consisting ofcervical cancer, ovarian cancer, endometrial cancer, uterine cancer andvulva cancer, the method comprising the steps of: (a) assaying a plasmaor tissue sample from the patient for the presence of CA 125, and at orabout the same time; and (b) assaying a bodily non-blood sample fromsaid patient for the presence of human chorionic gonadotropinbeta-subunit core fragment, wherein the detection of both CA 125 andhuman chorionic gonadotropin beta-subunit core fragment is an indicationof the presence of a gynecological cancer in the female. A measurementof the human chorionic gonadotropin beta-subunit core fragment alone wasstated to be useful in monitoring progression and regression of suchcancers.

Fung et al, “Biomarker for ovarian and endometrial cancer: hepcidin,” U.S. Patent Application 20070054329, published Mar. 8, 2007, describes amethod for qualifying ovarian and endometrial cancer status based onmeasuring hepcidin as a single biomarker, and based on panels of markersincluding hepcidin plus transthyretin, and those two markers plus atleast one biomarker selected from the group consisting of: Apo A1,transferrin, CTAP-IlI and ITIH4 fragment. An additional panel furtherincludes beta-2 microglobulin. These biomarkers were measured by massspectrometry, particularly SELDI-MS or by immunoassay. And data wasanalyzed by ROC curve analysis.

Fung et al. also described the use of hepcidin levels used incombination with other biomarkers, and concluded that the predictivepower of the test was improved. More specifically, increased levels ofhepcidin together with decreased levels transthyretin were correlatedwith ovarian cancer. Increased levels of hepcidin together withdecreased levels of transthyretin, together with levels of one or moreof Apo A1 (decreased level), transferrin (decreased level), CTAP-III(elevated level) and an internal fragment of ITIH4 (elevated level) werealso correlated with ovarian cancer. The foregoing biomarkers were tofurther be combined with beta-2 microglobulin (elevated level), CA125(elevated level) and/or other known ovarian cancer biomarkers for use inthe disclosed diagnostic test. And hepcidin was said to be hepcidin-25,transthyretin was said to be cysteinylated transthyretin, and/or ITIH4fragment perhaps being the ITIH4 fragment 1.

Diamandis, “Multiple marker assay for detection of ovarian cancer,” U.S.Patent Application 20060134120 published Jun. 22, 2006, described amethod for detecting a plurality of kallikrein markers associated withovarian cancer and optionally CA125, wherein the kallikrein markerscomprise or are selected from the group consisting of kallikrein 5,kallikrein 6, kallikrein 7, kallikrein 8, kallikrein 10, and kallikrein11. His patent application explained that a significant difference inlevels of these kallikreins, which are a subgroup of secreted serineproteases markers, and optionally that also of CA125, relative to thecorresponding normal levels, was indicative of ovarian cancer. Byrepeatedly sampling these markers in the same patient over time,Diamandis also found that a significant difference between the levels ofthe kallikrein markers, and optionally CA 125, in a later sample,relative to an earlier sample, is an indication that a patient's therapyis efficacious for inhibiting ovarian cancer. Samples were evaluated byprotein binding techniques, for example, immunoassays, and by nucleotidearray, PCR and the like techniques.

Gorelik et al, Multiplexed Immunobead-Based Cytokine Profiling for EarlyDetection of Ovarian Cancer” in Cancer Epidemiol Biomarkers Prev.2005:14(4) 981-7 (April 2005) reported that a panel of multiplecytokines that separately may not show strong correlation with thedisease provide diagnostic potential. A related patent applicationappears to be Lokshin et al., “Multifactorial assay for cancerdetection,” U. S. Patent Application 20050069963 published Mar. 31,2005. According to the journal article, a novel multianalyte LabMAPprofiling technology was employed that allowed simultaneous measurementof multiple markers. Various concentrations of 24 cytokines(cytokines/chemokines, growth, and angiogenic factors) in combinationwith CA-125 were measured in the blood sera of 44 patients withearly-stage ovarian cancer, 45 healthy women, and 37 patients withbenign pelvic tumors.

Of the cytokines discussed by Gorelik et al., six markers, specificallyinterleukin (IL)-6, IL-8, epidermal growth factor (EGF), vascularendothelial growth factor (VEGF), monocyte chemoattractant protein-1(MCP-1), together with CA-125, showed significant differences in serumconcentrations between ovarian cancer and control groups. Out of thosemarkers, IL-6, IL-8, VEGF, EGF, and CA-125, were used in aclassification tree analysis that reportedly resulted in 84% sensitivityat 95% specificity. The receiver operator characteristic curve (ROC)described using the combination of markers produced sensitivitiesbetween 90% and 100% and specificities of 80% to 90%. Interestingly, thereceiver operator characteristic curve for CA-125 alone resulted insensitivities of 70% to 80%. The classification tree analysis describedin the paper for discrimination of benign condition from ovarian cancerused CA-125, granulocyte colony-stimulating factor (G-CSF), IL-6, EGF,and VEGF which resulted in 86.5% sensitivity and 93.0/% specificity. Theauthors concluded that simultaneous testing of a panel of serumcytokines and CA-125 using LabMAP technology presented a promisingapproach for ovarian cancer detection.

A related patent application by Lokshin, “Enhanced diagnosticmultimarker serological profiling,” U. S. Patent Application 20070042405published Feb. 22, 2007 describes various biomarker panels andassociated methods for diagnosis of ovarian cancer. One method involvesdetermining the levels of at least four markers in the blood of apatient, where at least two different markers are selected from CA-125,prolactin, HE4 (human epididymis protein 4), sV-CAM and TSH; and where athird marker and a fourth marker are selected from CA-125, prolactin,IIE4, sV-CAM, TSH, cytokeratin, sI-CAM, IGFBP-1, eotaxin and FSH, whereeach of the third marker and fourth marker selected from the abovelisted markers is different from each other and different from either ofthe first and second markers, and where dysregulation of at least thefour markers indicates high specificity and sensitivity for a diagnosisof ovarian cancer. Another panel includes at least eight markers in theblood of a patient, wherein at least four different markers are selectedfrom the group consisting of CA-125, prolactin, HE4, sV-CAM, and TSH andwherein a fifth marker, a sixth marker, a seventh marker and an eighthmarker are selected from the group consisting of CA-125, prolactin, HE4,sV-CAM, TSH, cytokeratin, sI-CAM, IGFBP-1, eotaxin and FSH, and furtherwherein each of said fifth marker, said sixth marker, said seventhmarker and said eighth marker is different from the other and isdifferent from any of said at least four markers, wherein dysregulationof said at least eight markers indicates high specificity andsensitivity for a diagnosis of ovarian cancer.

The Lokshin (2007) patent application also describes a blood markerpanel comprising two or more of EGF (epidermal growth factor), G-CSF(granulocyte colony stimulating factor), IL-6, IL-8, CA-125 (CancerAntigen 125), VEGF (vascular endothelial growth factor), MCP-1 (monocytechemoattractant protein-1), anti-IL6, anti-IL8, anti-CA-125, anti-c-myc,anti-p53, anti-CEA, anti-CA 15-3, anti-MUC-1, anti-survivin, anti-bHCG,anti-osteopontin, anti-PDGF, anti-Her2/neu, anti-Akt1, anti-cytokeratin19, cytokeratin 19, EGFR, CEA, kallikrein-8, M-CSF, FasL, ErbB2 andHer2/neu in a sample of the patient's blood, where the presence of twoor more of the following conditions indicated the presence of ovariancancer in the patient: EGF (low), G-CSF (high), IL-6 (high), IL-8(high), VEGF (high), MCP-1 (low), anti-IL-6 (high), anti-IL-8 (high),anti-CA-125 (high), anti-c-myc (high), anti-p.sup.53 (high), anti-CEA(high), anti-CA 15-3 (high), anti-MUC-1 (high), anti-survivin (high),anti-bHCG (high), anti-osteopontin (high), anti-Her2/neu (high),anti-Akt1 (high), anti-cytokeratin 19 (high), anti-PDGF (high), CA-125(high), cytokeratin 19 (high), EGFR (low, Her2/neu (low), CEA (high),FasL (high), kallikrein-8 (low), ErbB2 (low) and M-CSF (low). Exemplarypanels include, without limitation: CA-125, cytokeratin-19, FasL, M-CSF;cytokeratin-19, CEA, Fas, EGFR, kallikrein-8; CEA, Fas, M-CSF, EGFR,CA-125; cytokeratin 19, kallikrein 8, CEA, CA 125, M-CSF; kallikrein-8,EGFR, CA-125; cytokeratin-19, CEA, CA-125, M-CSF, EGFR; cytokeratin-19,kallikrein-8, CA-125, M-CSF, FasL; cytokeratin-19, kallikrein-8, CEA,M-CSF; cytokeratin-19, kallikrein-8, CEA, CA-125; CA 125, cytokeratin19, ErbB2; EGF, G-CSF, IL-6, IL-8, VEGF and MCP-1; anti-CA 15-3,anti-IL-B, anti-survivin, anti-p53 and anti c-myc; anti-CA 15-3,anti-IL-B, anti-survivin, anti-p53, anti c-myc, anti-CEA, anti-IL-6,anti-EGF; and anti-bHCG.

Chan, et al., “Use of biomarkers for detecting ovarian cancer,” U.S.Published Patent Application 20050059013, published Mar. 17, 2005describes a method of qualifying ovarian cancer status in a subjectcomprising: (a) measuring at least one biomarker in a sample from thesubject, wherein the biomarker is selected from the group consisting ofApoA1, transthyretin .DELTA.N10, IAIH4 fragment, and combinationsthereof, and (b) correlating the measurement with ovarian cancer status.

Another embodiment in the Chan application described an additionalbiomarker selected from CA125, CA125 II, CA15-3, CA19-9, CA72-4, CA 195,tumor associated trypsin inhibitor (TATI), CEA, placental alkalinephosphatase (PLAP), Sialyl TN, galactosyltransferase, macrophage colonystimulating factor (M-CSF, CSF-1), lysophosphatidic acid (LPA), 110 kDcomponent of the extracellular domain of the epidermal growth factorreceptor (p110EGFR), tissue kallikreins, for example, kallikrein 6 andkallikrein 10 (NES-1), prostasin, HE4, crcatine kinase B (CKB), LASA,HER-2/neu, urinary gonadotropin peptide, Dianon NB 70/K, Tissue peptideantigen (TPA), osteopontin and haptoglobin, and protein variants (e.g.,cleavage forms, isoforms) of the markers.

An ELISA-based blood serum test described the evaluation of fourproteins useful in the early diagnosis of epithelial ovarian cancer(leptin, prolactin, osteopontin and insulin-like growth factor). Theauthors reported that no single protein could completely distinguish thecancer group from the healthy control group. However, the combination ofthese four proteins provided sensitivity 95%, positive predictive value(PPV) 95%, specificity 95%, and negative predictive value (NPV) 94%,which was said to be a considerable improvement on current methodology.Mor et al., “Serum protein markers for early detection of ovariancancer,” PNAS (102:21) 7677-7682 (2005).

A related patent application by Mor et al. “Identification of CancerProtein Biomarkers Using Proteomic Techniques,” U.S. Patent Application2005/0214826, published Sep. 29, 2005 describes biomarkers identified byusing a novel screening method. The biomarkers are stated todiscriminate between cancer and healthy subjects as well as being usefulin the prognosis and monitoring of cancer. Specifically, the abstract ofthe patent application relates to the use of leptin, prolactin, OPN andIGF-II for these purposes. The disclosed invention is somewhat moregenerally characterized as involving the comparison of expression of oneor more biomarkers in a sample that are selected from the groupconsisting of: 6Ckine, ACE, BDNF, CA125, E-Selectin, EGF, Eot2, ErbB1,follistatin, HCC4, HVEM, IGF-II, IGFBP-1, IL-17, IL-1srII, IL-2sRa,leptin, M-CSF R, MIF, MIP-1a, MIP3b, MMP-8, MMP7, MPTF-1, OPN, PARC,PDGF Rb, prolactin, ProteinC, TGF-b RIII, TNF-R1, TNF-a, VAP-1, VEGF R2and VEGF R3. A significant difference in the expression ofthese one ormore biomarkers in the sample as compared to a predetermined standard ofeach is said to diagnose or aid in the diagnosis of cancer.

A patent application by Le Page et al. “Methods of Diagnosing OvarianCancer and Kits Therefor,” WO2007/030949, published Mar. 22, 2007describes a method for determining whether a subject is affected byovarian cancer by detecting the expression levels of FGF-2 and CA125and, optionally, IL-18.

Other approaches described in the patent and scientific literatureinclude the analysis of expression of particular gene transcripts inblood cells. See, for example, Liew, “Method for the Detection of CancerRelated Gene Transcripts in Blood,” U.S. Published Patent Application2006/0134637, Jun. 22, 2006. Although gene transcripts specific forovarian cancer are not identified, transcripts from Tables 3J, 3K and 3Xare said to indicate the presence of cancer. See also, Tchagang et al.,“Early Detection of Ovarian Cancer Using Group Biomarkers,” Mol. CancerTher. (1):7 (2008).

Another diagnostic approach involves detecting circulating antibodiesdirected against tumor-associated antigens. See, Nelson et al. “AntigenPanels and Methods of Using the Same,” U.S. Patent Application2005/0221305, published Oct. 6, 2005; and Robertson “Cancer DetectionMethods and Regents,” U.S. Patent Application 2003/0232399, publishedDec. 18, 2003.

What has been urgently needed in the field of gynecologic oncology is aminimally invasive (preferably serum-based) clinical test for assessingand predicting the presence of ovarian cancer that is based on a robustset of biomarkers and sample features identified from a large anddiverse set of samples, together with methods and associated computersystems and software tools to predict, diagnose and monitor ovariancancer with high accuracy at its various stages.

SUMMARY OF THE INVENTION

The present invention generally relates to cancer biomarkers andparticularly to biomarkers associated with ovarian cancer. It providesmethods to predict, evaluate diagnose, and monitor cancer, particularlyovarian cancer, by measuring certain biomarkers, and further provides aset or array of reagents to evaluate the expression levels of biomarkersthat are associated with ovarian cancer. A preferred set of biomarkersprovides a detectable molecular signature of ovarian cancer in asubject. The invention provides a predictive or diagnostic test forovarian cancer, particularly for epithelial ovarian cancer and moreparticularly for early-stage ovarian cancer (that is Stage I, Stage IIor Stage I and II together).

More specifically, predictive tests and associated methods and productsalso provide useful clinical information regarding the stage of ovariancancer progression, that is: Stage I, Stage II, Stage III and Stage IVand an advanced stage which reflects relatively advanced tumors thatcannot readily be classified as either Stage m or Stage IV. Overall, theinvention also relates to newly discovered correlations between therelative levels of expression of certain groups of markers in bodilyfluids, preferably blood serum and plasma, and a subject's ovariancancer status.

In one embodiment, the invention provides a set of reagents to measurethe expression levels of a panel or set of biomarkers in a fluid sampledrawn from a patient, such as blood, serum, plasma, lymph, cerebrospinalfluid, ascites or urine. The reagents in a further embodiment are amultianalyte panel assay comprising reagents to evaluate the expressionlevels of these biomarker panels.

In embodiments of the invention, a subject's sample is prepared fromtissue samples such a tissue biopsy or from primary cell cultures orculture fluid. In a further embodiment, the expression of the biomarkersis determined at the polypeptide level. Related embodiments utilizeimmunoassays, enzyme-linked immunosorbent assays and multiplexedimmunoassays for this purpose.

Preferred panels of biomarkers are selected from the group consisting ofthe following sets of molecules and their measurable fragments: (a)myoglobin, CRP (C reactive protein), FGF basic protein and CA 19-9; (b)Hepatitis C NS4, Ribosomal P Antibody and CRP; (c) CA 19-9, TGF alpha,EN-RAGE, EGF and HSP 90 alpha antibody, (d) EN-RAGE, EGF, CA 125,Fibrinogen, Apolipoprotein CIII, EGF, Cholera Toxin and CA 19-9; (e)Proteinase 3 (cANCA) antibody, Fibrinogen, CA 125, EGF, CD40, TSH,Leptin, CA 19-9 and lymphotactin; (f) CA125, EGFR, CRP, IL-18,Apolipoprotein CIII, Tenascin C and Apolipoprotein A1; (g) CA125, Beta-2Microglobulin, CRP, Ferritin, TIMP-1, Creatine Kinase-MB and IL-8; (h)CA125, EGFR, IL-10, Haptoglobin, CRP, Insulin, TIMP-1, Ferritin, Alpha-2Macroglobulin, Leptin, IL-8, CTGF, EN-RAGE, Lymphotactin, TNF-alpha,IGF-1, TNF RII, von Willebrand Factor and MDC; (i) CA-125, CRP, EGF-R,CA-19-9, Apo-AI, Apo-CIII, TL-6, IL-18, MIP-1a, Tenascin C andMyoglobin; (j) CA-125, CRP, EGF-R, CA-19-9, Apo-AI, Apo-CIII, IL-6,MIP-1a, Tenascin C and Myoglobin; and (k) any of the biomarker panelspresented in Table II and Table III.

In another embodiment, the reagents that measure such biomarkers maymeasure other molecular species that are found upstream or downstream ina biochemical pathway or measure fragments of such biomarkers andmolecular species. In some instances, the same reagent may accuratelymeasure a biomarker and its fragments.

Another embodiment of the present invention relates to binding molecules(or binding reagents) to measure the biomarkers and related moleculesand fragments. Contemplated binding molecules includes antibodies, bothmonoclonal and polyclonal, aptamers and the like.

Other embodiments include such binding reagents provided in the form ofa test kit, optionally together with written instructions for performingan evaluation of biomarkers to predict the likelihood of ovarian cancerin a subject.

In other of its embodiments, the present invention provides methods ofpredicting the likelihood of ovarian cancer in a subject based ondetecting or measuring the levels in a specimen or biological samplefrom the subject of the foregoing biomarkers. As described in thisspecification, a change in the expression levels of these biomarkers,particularly their relative expression levels, as compared with acontrol group of patients who do not have ovarian cancer, is predictiveof ovarian cancer in that subject.

In other of its aspects, the type of ovarian cancer that is predicted isserous, endometrioid, mucinous, and clear cell tumors. And prediction ofovarian cancer includes the prediction of a specific stage of thedisease such as Stage I (IA, IB or IC), II, III and IV tumors.

In yet another embodiment, the invention relates to creating a reportfor a physician of the relative levels of the biomarkers and totransmitting such a report by mail, fax, email or otherwise. In anembodiment, a data stream is transmitted via the internet that containsthe reports of the biomarker evaluations. In a further embodiment, thereport includes the prediction as to the presence or absence of ovariancancer in the subject or the stratified risk of ovarian cancer for thesubject, optionally by subtype or stage of cancer.

According to another aspect of the invention, the foregoing evaluationof biomarker expression levels is combined for diagnostic purposes withother diagnostic procedures such as gastrointestinal tract evaluation,chest x-ray, HE4 test, CA-125 test, complete blood count, ultrasound orabdominal/pelvic computerized tomography, blood chemistry profile andliver function tests.

Yet other embodiments of the invention relate to the evaluation ofsamples drawn from a subject who is symptomatic for ovarian cancer or isat high risk for ovarian cancer. Other embodiments relate to subjectswho are asymptomatic of ovarian cancer. Symptomatic subjects have one ormore of the following: pelvic mass; ascites; abdominal distention;general abdominal discomfort and/or pain (gas, indigestion, pressure,swelling, bloating, cramps); nausea, diarrhea, constipation, or frequenturination; loss of appetite; feeling of fullness even after a lightmeal; weight gain or loss with no known reason; and abnormal bleedingfrom the vagina. The levels of biomarkers may be combined with thefindings of such symptoms for a diagnosis of ovarian cancer.

Embodiments of the invention are highly accurate for determining thepresence of ovarian cancer. By “highly accurate” is meant a sensitivityand a specificity each at least about 85 percent or higher, morepreferably at least about 90 percent or 92 percent and most preferablyat least about 95 percent or 97 percent accurate. Embodiments of theinvention further include methods having a sensitivity of at least about85 percent, 90 percent or 95 percent and a specificity of at least about55 percent, 65 percent, 75 percent, 85 percent or 90 percent or higher.Other embodiments include methods having a specificity of at least about85 percent, 90 percent or 95 percent, and a sensitivity of at leastabout 55 percent, 65 percent, 75 percent, 85 percent or 90 percent orhigher.

Embodiments of the invention relating sensitivity and specificity aredetermined for a population of subjects who are symptomatic for ovariancancer and have ovarian cancer as compared with a control group ofsubjects who are symptomatic for ovarian cancer but who do not haveovarian cancer. In another embodiment, sensitivity and specificity aredetermined for a population of subjects who are at increased risk forovarian cancer and have ovarian cancer as compared with a control groupof subjects who are at increased risk for ovarian cancer but who do nothave ovarian cancer. And in another embodiment, sensitivity andspecificity are determined for a population of subjects who aresymptomatic for ovarian cancer and have ovarian cancer as compared witha control group of subjects who are not symptomatic for ovarian cancerbut who do not have ovarian cancer.

In other aspects, the levels of the biomarkers are evaluated by applyinga statistical method such as knowledge discovery engine (KDE™),regression analysis, discriminant analysis, classification treeanalysis, random forests, ProteomeQuest®, support vector machine, One R,kNN and heuristic naive Bayes analysis, neural nets and variantsthereof.

In another embodiment, a predictive or diagnostic model based on theexpression levels of the biomarkers is provided. The model may be in theform of software code, computer readable format or in the form ofwritten instructions for evaluating the relative expression of thebiomarkers.

A patient's physician can utilize a report of the biomarker evaluation,in a broader diagnostic context, in order to develop a relatively morecomplete assessment of the risk that a given patient has ovarian cancer.In making this assessment, a physician will consider the clinicalpresentation of a patient, which includes symptoms such as a suspiciouspelvic mass and/or ascites, abdominal distention and other symptomswithout another obvious source of malignancy. The general lab workup forsymptomatic patients currently includes a GI evaluation if clinicallyindicated, chest x-ray, CA-125 test, CBC, ultrasound or abdominal/pelvicCT if clinically indicated, chemistry profile with LFTs and may includea family history evaluation along with genetic marker tests such asBRCA-1 and BRCA-2. (See, generally, the NCCN Clinical PracticeGuidelines in Oncology™ for Ovarian Cancer, V.1.2007.)

The present invention provides a novel and important additional sourceof information to assist a physician in stratifying a patient's risk ofhaving ovarian cancer and in planning the next diagnostic steps to take.The present invention is also similarly useful in assessing the risk ofovarian cancer in non-symptomatic, high-risk subjects as well as for thegeneral population as a screening tool. It is contemplated that themethods of the present invention may be used by clinicians as part of anoverall assessment of other predictive and diagnostic indicators.

The present invention also provides methods to assess the therapeuticefficacy of existing and candidate chemotherapeutic agents and othertypes of cancer treatments. As will be appreciated by persons skilled inthe art, the relative expression levels of the biomarker panels—orbiomarker profiles—are determined as described above, in specimens takenfrom a subject prior to and again after treatment or, optionally, atprogressive stages during treatment. A change in the relative expressionof these biomarkers to a non-cancer profile of expression levels (or toa more nearly non-cancer expression profile) or to a stable,non-changing profile of relative biomarker expression levels isinterpreted as therapeutic efficacy. Persons skilled in the art willreadily understand that a profile of such expressions levels may becomediagnostic for cancer or a pre-cancer, pre-malignant condition or simplymove toward such a diagnostic profile as the relative ratios of thebiomarkers become more like a cancer-related profile than previously.

In another embodiment, the invention provides a method for determiningwhether a subject potentially is developing cancer. The relative levelsof expression of the biomarkers are determined in specimens taken from asubject over time, whereby a change in the biomarker expression profiletoward a cancer profile is interpreted as a progression towarddeveloping cancer.

The expression levels of the biomarkers of a specimen may be storedelectronically once a subject's analysis is completed and recalled forsuch comparison purposes at a future time.

The present invention further provides methods, software products,computer systems and networks, and associated instruments that provide ahighly accurate test for ovarian cancer.

The combinations of markers described in this specification providesensitive, specific and accurate methods for predicting the presence ofor detecting ovarian cancer at various stages of its progression. Theevaluation of samples as described may also correlate with the presenceof a pre-malignant or a pre-clinical condition in a patient. Thus, it iscontemplated that the disclosed methods are useful for predicting ordetecting the presence of ovarian cancer in a sample, the absence ofovarian cancer in a sample drawn from a subject, the stage of an ovariancancer, the grade of an ovarian cancer, the benign or malignant natureof an ovarian cancer, the metastatic potential of an ovarian cancer, thehistological type of neoplasm associated with the ovarian cancer, theindolence or aggressiveness of the cancer, and other characteristics ofovarian cancer that are relevant to prevention, diagnosis,characterization, and therapy of ovarian cancer in a patient.

It is further contemplated that the methods disclosed are also usefulfor assessing the efficacy of one or more test agents for inhibitingovarian cancer, assessing the efficacy of a therapy for ovarian cancer,monitoring the progression of ovarian cancer, selecting an agent ortherapy for inhibiting ovarian cancer, monitoring the treatment of apatient afflicted with ovarian cancer, monitoring the inhibition ofovarian cancer in a patient, and assessing the carcinogenic potential ofa test compound by evaluating biomarkers of test animals followingexposure.

DETAILED DESCRIPTION

The biomarker panels and associated methods and products were identifiedthrough the analysis of analyte levels of various molecular species inhuman blood serum drawn from subjects having ovarian cancer of variousstages and subtypes, subjects having non-cancer gynecological disordersand normal subjects. The immunoassays described below were courteouslyperformed by our colleagues at Rules-Based Medicine of Austin, Tex.using their Multi-Analyte Profile (MAP) Luminex® platform(www.rulesbasedmedicine.com).

While a preferred sample is blood serum, it is contemplated that anappropriate sample can be derived from any biological source or sample,such as tissues, extracts, cell cultures, including cells (for example,tumor cells), cell lysates, and physiological fluids, such as, forexample, whole blood, plasma, serum, saliva, ductal lavage, ocular lensfluid, cerebral spinal fluid, sweat, urine, milk, asciles fluid,synovial fluid, peritoneal fluid and the like. The sample can beobtained from animals, preferably mammals, more preferably primates, andmost preferably humans using species specific binding agents that areequivalent to those discussed below in the context of human sampleanalysis. It is further contemplated that these techniques and markerpanels may be used to evaluate drug therapy in rodents and otheranimals, including transgenic animals, relevant to the development ofhuman and veterinary therapeutics.

The sample can be treated prior to use by conventional techniques, suchas preparing plasma from blood, diluting viscous fluids, and the like.Methods of sample treatment can involve filtration, distillation,extraction, concentration, inactivation of interfering components,addition of chaotropes, the addition of reagents, and the like. Nucleicacids (including silencer, regulatory and interfering RNA) may beisolated and their levels of expression for the analytes described belowalso used in the methods of the invention.

Samples and Analytical Platform.

The set of blood serum samples that was analyzed to generate most of thedata discussed below contained 150 ovarian cancer samples and 150non-ovarian cancer samples. Subsets of these samples were used asdescribed. The ovarian cancer sample samples further comprised thefollowing epithelial ovarian cancer subtypes: serous (64), clear cell(22), endometrioid (35), mucinous (15), mixed, that is, consisting ofmore than one subtype (14). The stage distribution of the ovarian cancersamples was: Stage I (41), Stage II (23), Stage III (68), Stage IV (12)and unknown stage (6).

The non-ovarian cancer sample set includes the following ovarianconditions: benign (104), normal ovary (29) and “low malignantpotential/borderline (3). The sample set also includes serum frompatients with other cancers: cervical cancer (7), endometrial cancer (6)and uterine cancer (1).

Analyte levels in the samples discussed in this specification weremeasured using a high-throughput, multi-analyte immunoassay platform. Apreferred platform is the Luminex®MAP system as developed by Rules-BasedMedicine, Inc. in Austin, Tex. It is described on the company's websiteand also, for example, in publications such as Chandler et al., “Methodsand kits for the diagnosis of acute coronary syndrome, U. S. PatentApplication 2007/0003981, published Jan. 4, 2007, and a relatedapplication of Spain et al., “Universal Shotgun Assay,” UI. S. PatentApplication 2005/0221363, published Oct. 6, 2005. This platform haspreviously been described in Lokshin (2007) and generated data used inother analyses of ovarian cancer biomarkers. However, any immunoassayplatform or system may be used.

In brief, to describe a preferred analyte measurement system, the MAPplatform incorporates polystyrene microspheres that are dyed internallywith two spectrally distinct fluorochromes. By using accurate ratios ofthe fluorochromes, an array is created consisting of 100 differentmicrosphere sets with specific spectral addresses. Each microsphere setcan display a different surface reactant. Because microsphere sets canbe distinguished by their spectral addresses, they can be combined,allowing up to 100 different analytes to be measured simultaneously in asingle reaction vessel. A third fluorochrome coupled to a reportermolecule quantifies the biomolecular interaction that has occurred atthe microsphere surface. Microspheres are interrogated individually in arapidly flowing fluid stream as they pass by two separate lasers in theLuminex® analyzer. High-speed digital signal processing classifies themicrosphere based on its spectral address and quantifies the reaction onthe surface in a few seconds per sample.

Skilled artisans will recognize that a wide variety of analyticaltechniques may be used to determine the levels of biomarkers in a sampleas is described and claimed in this specification. Other types ofbinding reagents available to persons skilled in the art may be utilizedto measure the levels of the indicated analytes in a sample. Forexample, a variety of binding agents or binding reagents appropriate toevaluate the levels of a given analyte may readily be identified in thescientific literature. Generally, an appropriate binding agent will bindspecifically to an analyte, in other words, it reacts at a detectablelevel with the analyte but does not react detectably (or reacts withlimited cross-reactivity) with other or unrelated analytes. It iscontemplated that appropriate binding agents include polyclonal andmonoclonal antibodies, aptamers, RNA molecules and the like.Spectrometric methods also may be used to measure the levels ofanalytes, including immunofluorescence, mass spectrometry, nuclearmagnetic resonance and optical spectrometric methods. Depending on thebinding agent to be utilized, the samples may be processed, for example,by dilution, purification, denaturation, digestion, fragmentation andthe like before analysis as would be known to persons skilled in theart. Also, gene expression, for example, in a tumor cell or lymphocytealso may be determined.

It is also contemplated that the identified biomarkers may have multipleepitopes for immunassays and/or binding sites for other types of bindingagents. Thus, it is contemplated that peptide fragments or otherepitopes of the identified biomarkers, isoforms of specific proteins andeven compounds upstream or downstream in a biological pathway or thathave been post-translationally modified may be substituted for theidentified analytes or biomarkers so long as the relevant and relativestoichiometries are taken into account appropriately. Skilled artisanswill recognize that alternative antibodies and binding agents can beused to determine the levels of any particular analyte, so long as theirvarious specificities and binding affinities are factored into theanalysis.

A variety of algorithms may be used to measure or determine the levelsof expression of the analytes or biomarkers used in the methods and testkits of the present invention. It is generally contemplated that suchalgorithms will be capable of measuring analyte levels beyond themeasurement of simple cut-off values. Thus, it is contemplated that theresults of such algorithms will generically be classified asmultivariate index analyses by the U.S. Food and Drug Administration.Specific types of algorithms include: knowledge discovery engine (KDE™),regression analysis, discriminant analysis, classification treeanalysis, random forests, ProteomeQuest®, support vector machine, One R,kNN and heuristic naive Bayes analysis, neural nets and variantsthereof.

Analysis and Examples

The following discussion and examples are provided to describe andillustrate the present invention. As such, they should not be construedto limit the scope of the invention. Those skilled in the art will wellappreciate that many other embodiments also fall within the scope of theinvention, as it is described in this specification and the claims.

Analysis of Data to Find Informative Biomarker Panels Using the KDE™.

Correlogic has described the use of evolutionary and pattern recognitionalgorithms in evaluating complex data sets, including the KnowledgeDiscovery Engine (KDE™) and ProteomeQueste®. See, for example, Hitt etal., U.S. Pat. No. 6,925,389, “Process for Discriminating BetweenBiological States Based on Hidden Patterns From Biological Data” (issuedAug. 2, 2005); Hitt, U.S. Pat. No. 7,096,206, “Heuristic Method ofClassification,” (issued Aug. 22, 2006) and Hitt, U.S. Pat. No.7,240,038, “Heuristic Method of Classification,” (to be issued Jul. 3,2007). The use of this technology to evaluate mass spectral data derivedfrom ovarian cancer samples is further elucidated in Hitt et al.,“Multiple high-resolution serum proteomic features for ovarian cancerdetection,” U. S. Published Patent Application 2006/0064253, publishedMar. 23, 2006.

When analyzing the data set by Correlogic's Knowledge Discovery Engine,the following five-biomarker panels were found to provide sensitivitiesand specificities for various stages of ovarian cancer as set forth inTable 1. Specifically, KDE Model 1 [2_0008_20] returned a relativelyhigh accuracy for Stage I ovarian cancer and included these markers:Cancer Antigen 19-9 (CA19-9, Swiss-Prot Accession Number: Q9BXJ9), CReactive Protein (CRP, Swiss-Prot Accession Number: P02741), FibroblastGrowth Factor-basic Protein (FGF-basic, Swiss-Prot Accession Number:P09038) and Myoglobin (Swiss-Prot Accession Number: P02144). KDE Model 2[4_0002-10] returned a relatively high accuracy for Stage III, IV and“advanced” ovarian cancer and included these markers: Hepatitis C NS4Antibody (Hep C NS4 Ab), Ribosomal P Antibody and CRP. KDE Model 3[4_0009_140] returned a relatively high accuracy for Stage I andincluded these markers: CA 19-9, TGF alpha, EN-RAGE (Swiss-ProtAccession Number: P80511), Epidermal Growth Factor (EGF, Swiss-ProtAccession Number: P01133) and IISP 90 alpha antibody. KDE Model 4[4_0026_100] returned a relatively high accuracy for Stage II and StagesIII, IV and “advanced” ovarian cancers and included these markers:EN-RAGE, EGF, Cancer Antigen 125 (CA125, Swiss-Prot Accession Number:Q14596), Fibrinogen (Swiss-Prot Accession Number: Alpha chain P02671;Beta chain P02675; Gamma chain P02679), Apolipoprotein CIII (ApoCIII,Swiss-Prot Accession Number: P02656), Cholera Toxin and CA 19-9. KDEModel 5 [4_0027_20] also returned a relatively high accuracy for StageII and Stages III, IV and “advanced” ovarian cancers and included thesemarkers: Proteinase 3 (cANCA) antibody, Fibrinogen, CA 125, BGF, CD40(Swiss-Prot Accession Number: Q6P2H9), Thyroid Stimulating Hormone (TSH,Swiss-Prot Accession Number: Alpha P01215; Beta P01222 P02679, Leptin(Swiss-Prot Accession Number: P41159), CA 19-9 and Lymphotactin(Swiss-Prot Accession Number: P47992). It is contemplated that skilledartisans could use the KDE analytical tools to identify other,potentially useful sets of biomarkers for predictive or diagnostic valuebased on the levels of selected analytes. Note that the KDE algorithmmay select and utilize various markers based on their relativeabundances; and that a given marker, for example the level of choleratoxin in Model IV may be zero but is relevant in combination with theother markers selected in a particular grouping.

Skilled artisans will recognize that a limited size data set as was usedin this specification may lead to different results, for example,different panels of markers and varying accuracies when comparing therelative performance of KDE with other analytical techniques to identifyinformative panels of biomarkers. These particular KDE models were builton a relatively small data set using 40 stage I ovarian cancers and 40normal/benigns and were tested blindly on the balance of the stage II,II/IV described above. Thus, the specificity is of the stage I samplesreflects sample set size and potential overfitting. The drop inspecificity for the balance of the non-ovarian cancer samples also isexpected given the relatively larger size of the testing set relative tothe training set. Overall, the biomarker panel developed for the stage Isamples also provides potentially useful predictive and diagnosticassays for later stages of ovarian cancer given the high sensitivityvalues.

However, these examples of biomarker panels illustrate that there are anumber of parameters that can be adjusted to impact model performance.For instance in these cases a variety of different numbers of featuresare combined together, a variety of match values are used, a variety ofdifferent lengths of evolution of the genetic algorithm are used andmodels differing in the number of nodes are generated. By routineexperimentation apparent to one skilled in the art, combinations ofthese parameters can be used to generate other predictive models basedon biomarker panels having clinically relevant performance.

TABLE I Results of Analysis Using Knowledge Discovery Engine to developa stage I specific classification model. Sensitivity SpecificityAccuracy Sensitivity Sensitivity Model Name Feature Match GenerationNode Stage I Stage I Stage I Stage II Stage III-IV Specificity 2_0008_204 0.9 20 12 75 100 87.5 60.9 46.5 82.6 4_0002_10 3 0.7 10 4 75 100 87.569.6 82.6 56 4_0009_140 5 0.6 140 5 75 100 87.5 43.5 39.5 71.64_0026_100 9 0.7 100 5 87.5 100 93.8 78.3 84.9 67 4_0027_20 9 0.8 20 587.5 100 93.8 78.3 84.9 60.6

Methods and Analysis to Find Informative Biomarker Panels Using RandomForests.

A preferred analytical technique, known to skilled artisans, is that ofBreiman, Random Forests. Machine Learning, 2001.45:5-32; as furtherdescribed by Segel, Machine Learning Benchmarks and Random ForestRegression, 2004; and Robnik-Sikonja, Improving Random Forests, inMachine Learning, ECML, 2004 Proceedings, J. F. B. e. al., Editor, 2004,Springer. Berlin. Other variants of Random Forests are also useful andcontemplated for the methods of the present invention, for example,Regression Forests, Survival Forests, and weighted population RandomForests.

A modeling set of samples was used as described above for diagnosticmodels built with the KDE algorithm. Since each of the analyte assays isan independent measurement of a variable, under some circumstances,known to those skilled in the art, it is appropriate to scale the datato adjust for the differing variances of each assay. In such cases,biweight, MAD or equivalent scaling would be appropriate, although insome cases, scaling would not be expected to have a significant impact.A bootstrap layer on top of the Random Forests was used in obtaining theresults discussed below.

In preferred embodiments of the present invention, contemplated panelsof biomarkers are:

a. Cancer Antigen 125 (CA125, Swiss-Prot Accession Number: Q14596) andEpidermal Growth Factor Receptor (EGF-R, Swiss-Prot Accession Number:P00533).

b. CA125 and C Reactive Protein (CRP, Swiss-Prot Accession Number:P02741).

c. CA125, CRP and EGF-R.

d. Any one or more of CA125, CRP and EGF-R, plus any one or more ofFerritin (Swiss-Prot Accession Number: Heavy chain P02794; Light chainP02792), Interklukin-8 (IL-8, Swiss-Prot Accession Number: P10145), andTissue Inhibitor of Metalloproteinases 1 (TIMP-1, Swiss-Prot AccessionNumber: P01033),

e. Any one of the biomarker panels presented in Table II and Table 111.

f. Any of the foregoing panels of biomarkers (a-e) plus any one or moreof the other biomarkers in the following list if not previously includedin the foregoing panels (a-e). These additional biomarkers wereidentified empirically or by a literature review: Alpha-2 Macroglobulin(A2M, Swiss-Prot Accession Number: P01023), Apolipoprotein A1-1 (ApoA1,Swiss-Prot Accession Number: P02647), Apolipoprotein C-III (ApoCIII,Swiss-Prot Accession Number: P02656), Apolipoprotein H (ApoH, Swiss-ProtAccession Number: P02749), Beta-2 Microglobulin (B2M, Swiss-ProtAccession Number: P23560), Betacellulin (Swiss-Prot Accession Number:P35070), C Reactive Protein (CRP, Swiss-Prot Accession Number: P02741).Cancer Antigen 19-9 (CA 19-9, Swiss-Prot Accession Number: Q9BXJ9),Cancer Antigen 125 (CA125, Swiss-Prot Accession Number: Q14596),Collagen Type 2 Antibody, Creatine Kinase-MB (CK-MB, Swiss-ProtAccession Number: Brain P12277; Muscle P06732), C Reactive Protein (CRP,Swiss-Prot Accession Number: P02741), Connective Tissue Growth Factor(CTGF, Swiss-Prot Accession Number: P29279), Double Stranded DNAAntibody (dsDNA Ab), EN-RAGE (Swiss-Prot Accession Number: P80511),Eotaxin (C-C motif chemokine 11, small-inducible cytokine A11 andEosinophil chemotactic protein, Swiss-Prot Accession Number: P51671),Epidermal Growth Factor Receptor (EGF-R, Swiss-Prut Accession Number:P00533), Ferritin (Swiss-Prot Accession Number: Heavy chain P02794;Light chain P02792), Follicle-stimulating hormone (FSH,Follicle-stimulating hormone beta subunit, FSH-beta, FSH-B, Follitropinbeta chain, Follitropin subunit beta, Swiss-Prot Accession Number:P01225), Haptoglobin (Swiss-Prot Accession Number: P00738), HE4 (Majorepididymis-specific protein E4, Epididymal secretory protein E4,Putative protease inhibitor WAP5 and WAP four-disulfide core domainprotein 2, Swiss-Prot Accession Number: Q14508), Insulin (Swiss-ProtAccession Number: P01308), Insulin-like Growth Factor 1 (IGF-1,Swiss-Prot Accession Number: P01343), Insulin like growth factor II(IGF-II, Somatomedin-A, Swiss-Prot Accession Number: P01344), InsulinFactor VII (Swiss-Prot Accession Number: P08709), Interleukin-6 (IL-6,Swiss-Prot Accession Number: P05231), Interleukin-8 (IL-8, Swiss-ProtAccession Number: P10145), Interleukin-10 (IL-10, Swiss-Prot AccessionNumber: P22301), Interleukin-18 (IL-18, Swiss-Prot Accession Number:Q14116), Leptin (Swiss-Prot Accession Number: P41159), Lymphotactin(Swiss-Prot Accession Number P47992), Macrophage-derived Chemokine (MDC,Swiss-Prot Accession Number 000626), Macrophage Inhibitory Factor (SWISSPROT), Macrophage Inflammatory Protein 1 alpha (MIP-1alpha, Swiss-ProtAccession Number: P10147), Macrophage migration inhibitory factor (MIF,Phenylpyruvate tautomerase, Glycosylation-inhibiting factor, GIF,Swiss-Prot Accession Number: P14174), Myoglobin (Swiss-Prot AccessionNumber: P02144), Ostopontin (Bone sialoprotein 1, Secretedphosphoprotein 1, SPP-1, Urinary stone protein, Nephropontin, Uropontin,Swiss-Prot Accession Number: P10451), Pancreatic Islet Cells (GAD)Antibody, Prolactin (Swiss-Prot Accession Number: P01236), Stem CellFactor (SCF, Swiss-Prot Accession Number: P21583), Tenascin C(Swiss-Prot Accession Number: P24821), Tissue Inhibitor ofMetalloproteinases 1 (TIMP-1, Swiss-Prot Accession Number: P01033),Tumor Necrosis Factor-alpha (TNF-alpha, Swiss-Prot Accession Number:P01375), Tumor Necrosis Factor RII (TNF-RII, Swiss-Prot AccessionNumber: Q92956), von Willebrand Factor (vWF, Swiss-Prot AccessionNumber: P04275) and the other biomarkers identified as being informativefor cancer in the references cited in this specification.

Using the Random Forests analytical approach, a preferred sevenbiomarker panel was identified that has a high predictive value forStage I ovarian cancer. It includes: ApoA1, ApoCIII, CA125, CRP, EGF-R,IL-18 and Tenascin. In the course of building and selecting therelatively more accurate models for Stage I cancers generated by RandomForests using these biomarkers, the sensitivity for Stage I ovariancancers ranged from about 80% to about 85%. Sensitivity was also about95 for Stage II and about 94% sensitive for Stage III/IV. The overallspecificity was about 70%.

Similarly, a preferred seven biomarker panel was identified that has ahigh predictive value for Stage II. It includes: B2M, CA125, CK-MB, CRP,Ferritin, IL-8 and TIMP1. A preferred model for Stage II had asensitivity of about 82% and a specificity of about 88%.

For Stage III, Stage IV and advanced ovarian cancer, the following 19biomarker panel was identified: A2M, CA125, CRP, CTGF, EGF-R, EN-RAGE,Ferritin, Haptoglobin, IGF-1, IL-8, IL-10, Insulin, Leptin,Lymphotactin, MDC, TIMP-1, TNF-alpha, TNF-RII, vWF. A preferred modelfor Stage III/IV had a sensitivity of about 86% and a specificity ofabout 89%.

Other preferred biomarker or analyte panels for detecting, diagnosingand monitoring ovarian cancer are shown in Table II and in Table III.These panels include CA-125, CRP and EGF-R and, in most cases, CA19-9.In Table II, 20 such panels of seven analytes each selected from 20preferred analytes are displayed in columns numbered 1 through 20. InTable III, another 20 such panels of seven analytes each selected from23 preferred analytes are displayed in columns numbered 1 through 20.

TABLE II Additional Biomarker Panels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1516 17 18 19 20 CA125 x x x x x x x x x x x x x x x x x x x x CRP x x x xx x x x x x x x x x x x x x x x EGF-R x x x x x x x x x x x x x x x x xx x x CA19-9 x x x x x x x x x x x x x x x x x x x Haptoglobin SerumAmyloid P x x x Apo A1 x x IL-6 x x x x x x Myoglobin x x x x x x x x xx x MIP-1□ x x x x x x x x x x x x EN-RAGE CK-MB vWF x x x Leptin x xApo CIII x x x Growth Hormone x x x x x x IL-10 IL-18 x x x x x x x xMyeloperoxidase x x VCAM-1 x x x

TABLE III Additional Biomarker Panels 1 2 3 4 5 6 7 8 9 10 11 12 13 1415 16 17 18 19 20 CA125 x x x x x x x x x x x x x x x x x x x x CRP x xx x x x x x x x x x x x x x x x x x EGF-R x x x x x x x x x x x x x x xx x x x x CA19-9 x x x x x x x x x x x x x x x x x x x Haptoglobin SerumAmyloid P x x x Apo A1 x x 1L-6 x x x x x x Myoglobin x x x x x x x x xx MIP-1□ x x x x x x x x x x x x x x EN-RAGE CK-MB x vWF x x x x Leptinx x x Apo CIII x x x x x x Growth Hormone IL-10 x x IL-18Myeloperoxidase x x x VCAM-1 Insulin x Ferritin x x x x x Haptoglobin x

Other preferred biomarker panels (or models) for all stages of ovariancancer include: (a) CA-125, CRP, EGF-R, CA-19-9, Apo-AI, Apo-CIII, IL-6,IL-18, MIP-1a, Tenascin C and Myoglobin; (b) CA125, CRP, CA19-9, EGF-R,Myoglobin, IL-18, Apo CIII; and (c) CA125, CRP, EGF-R, CA19-9, Apo CIII,MTP-1a, Myoglobin, IL-18, IL-6, Apo AI, Tenascin C, vWF, Haptoglobin,IL-10. Optionally, any one or more of the following biomarkers may beadded to these or to any of the other biomarker panels disclosed abovein text or tables (to the extent that any such panels are not alreadyspecifically identified therein): vWF, Haptoglobin, IL-10, IGF-I,IGF-II, Prolactin, HE4, ACE, ASP and Resistin.

Any two or more of the preferred biomarkers described above will havepredictive value, however, adding one or more of the other preferredmarkers to any of the analytical panels described herein may increasethe panel's predictive value for clinical purposes. For example, addingone or more of the different biomarkers listed above or otherwiseidentified in the references cited in this specification may alsoincrease the biomarker panel's predictive value and are thereforeexpressly contemplated. Skilled artisans can readily assess the utilityof such additional biomarkers. It is contemplated that additionalbiomarker appropriate for addition to the sets (or panels) of biomarkersdisclosed or claimed in this specification will not result in a decreasein either sensitivity or specificity without a corresponding increase ineither sensitivity or specificity or without a corresponding increase inrobustness of the biomarker panel overall. A sensitivity and/orspecificity of at least about 80% or higher are preferred, morepreferably at least about 85% or higher, and most preferably at leastabout 90% or 95% or higher.

To practice the methods of the present invention, appropriate cut-offlevels for each of the biomarker analytes must be determined for cancersamples in comparison with control samples. As discussed above, it ispreferred that at least about 40 cancer samples and 40 benign samples(including benign, non-malignant disease and normal subjects) be usedfor this purpose, preferably case matched by age, sex and gender. Largersample sets are preferred. A person skilled in the art would measure thelevel of each biomarker in the selected biomarker panel and then use analgorithm, preferably such as Random Forest, to compare the level ofanalytes in the cancer samples with the level of analytes in the controlsamples. In this way, a predictive profile can be prepared based oninformative cutoffs for the relevant disease type. The use of a separatevalidation set of samples is preferred to confirm the cut-off values sodetermined. Case and control samples can be obtained by obtainingconsented (or anonymized) samples in a clinical trial or fromrepositories like the Screening Study for Prostate, Lung, Colorectal,and Ovarian Cancer—PLCO Trial sponsored by the National Cancer Institute(http://www.cancer.gov/clinicaltrials/PLCO-1) or The GynecologicOncology Group (http://www.gog.org/). Samples obtained in multiple sitesare also preferred.

The results of analysis of patients' specimens using the disclosedpredictive biomarker panels may be output for the benefit of the user ordiagnostician, or may otherwise be displayed on a medium such as, butnot limited to, a computer screen, a computer readable medium, a pieceof paper, or any other visible medium.

The foregoing embodiments and advantages of this invention are setforth, in part, in the preceding description and examples and, in part,will be apparent to persons skilled in the art from this description andexamples and may be further realized from practicing the invention asdisclosed herein. For example, the techniques of the present inventionare readily applicable to monitoring the progression of ovarian cancerin an individual, by evaluating a specimen or biological sample asdescribed above and then repeating the evaluation at one or more laterpoints in time, such that a difference in the expression ordisregulation of the relevant biomarkers over time is indicative of theprogression of the ovarian cancer in that individual or theresponsiveness to therapy. All references, patents, journal articles,web pages and other documents identified in this patent application arehereby incorporated by reference in their entireties.

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1. A set of reagents to measure the levels of biomarkers in a specimen,wherein the biomarkers are selected from the group consisting of thefollowing panels of biomarkers and their measurable fragments: (a)CA125, Apo A1, HE4, and FSH; (b) CA125, Apo A1, HE4, FSH, and IL-6; (c)CA125, Apo A1, HE4, FSH, and EGFR; (d) CA125, Apo A1, HE4, FSH, and CRP;(e) CA125, Apo A1, HE4, FSH, and transferrin; (f) CA125, Apo A1, HE4,FSH, and transthyretin; (g) CA125, Apo A1, HE4, FSH, IL-6, andtransferrin; (h) CA125, Apo A1, HE4, FSH, EGFR, and transferrin; (i)CA125, Apo A1, HE4, FSH, CRP, and transferrin; (j) CA125, Apo A1, HE4,FSH, IL-6, and transthyretin; (k) CA125, Apo A1, HE4, FSH, EGFR, andtransthyretin; (l) CA125, Apo A1, HE4, FSH, CRP, and transthyretin; (m)CA125, Apo A1, Beta-2 Microglobulin, and FSH; (n) CA125, Apo A1, Beta-2Microglobulin, FSH, and IL-6; (o) CA125, Apo A1, Beta-2 Microglobulin,FSH, and EGFR; (p) CA125, Apo A1, Beta-2 Microglobulin, FSH, and CRP;(q) CA125, Apo A1, transferrin, and FSH; (r) CA125, Apo A1, transferrin,FSH, and IL-6; (s) CA125, Apo A1, transferrin, FSH, and EGFR; (t) CA125,Apo A1, transferrin, FSH, and CRP; (u) CA125, Apo A1, transthyretin, andFSH; (v) CA125, Apo A1, transthyretin, FSH, and IL-6; (w) CA125, Apo A1,transthyretin, FSH, and EGFR; (x) CA125, Apo A1, transthyretin, FSH, andCRP; (y) CA125, Apo A1, Beta-2 Microglobulin, transferrin,transthyretin, and FSH.
 2. (canceled)
 3. The set of reagents of claim 1,wherein the reagents are biding molecules.
 4. The set of reagents ofclaim 3, wherein the binding molecules are antibodies.
 5. A test kitcomprising the set of reagents of claim
 1. 6. A method of predicting thelikelihood of cancer in a subject, comprising: detecting the levels ofbiomarkers in a specimen using the set of reagents of claim 1, wherein achange in the levels of the biomarkers, as compared with a control groupof patients who do not have cancer, is predictive of cancer in thatsubject.
 7. The method of claim 6, wherein the cancer is ovarian cancer.8. The method of claim 7, wherein a change in the relative levels of thebiomarkers is determined.
 9. The method of claim 7, wherein the specimenis selected from the group consisting of blood, serum, plasma, lymph,cerebrospinal fluid, ascites, urine and tissue biopsy.
 10. The method ofclaim 7, wherein the ovarian cancer is selected from the groupconsisting of serous, endometrioid, mucinous, and clear cell cancer. 11.The method of claim 7, wherein the prediction of ovarian cancer includesa stage selected from the group consisting of Stage IA, IB, IC, II, IIIand IV tumors.
 12. The method of claim 7, further comprising creating areport of the relative levels of the biomarkers.
 13. The method of claim12, wherein the report includes the prediction as to the presence orabsence of ovarian cancer in the subject or the stratified risk ofovarian cancer for the subject, optionally by stage of cancer.
 14. Themethod of claim 7, wherein the sample is taken from a subject selectedfrom the group consisting of subjects who are symptomatic for ovariancancer and subjects who are at high risk for ovarian cancer.
 15. Themethod of claim 7, wherein the method has a sensitivity of at leastabout 85 percent and a specificity of at least about 85 percent.
 16. Themethod of claim 15, wherein the sensitivity and specificity aredetermined for a population of women who are symptomatic for ovariancancer and have ovarian cancer as compared with a control group of womenwho are symptomatic for ovarian cancer but who do not have ovariancancer.
 17. A predictive or diagnostic model based on levels of thepanels of biomarkers of claim
 1. 18. A multianalyte panel assaycomprising the set of reagents of claim
 1. 19. A method to assess thetherapeutic efficacy of a cancer treatment, comprising: comparing thebiomarker profiles in specimens taken from a subject before and afterthe treatment or during the course of treatment with a set of reagentsaccording to claim 1, wherein a change in the biomarker profile overtime toward a non-cancer profile or to a stable profile is interpretedas efficacy.
 20. A method for determining whether a subject potentiallyis developing cancer, comprising: comparing the biomarker profiles inspecimens taken from a subject at two or more points in time with a setof reagents according to claim 1, wherein a change in the biomarkerprofile toward a cancer profile, is interpreted as a progression towarddeveloping cancer.